• Title, Summary, Keyword: User Generated Text

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Text Watermarking using Space Coding (Space Coding을 이용한 Text watermarking)

  • 황미란;추현곤;최종욱;김회율
    • Proceedings of the IEEK Conference
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    • pp.117-120
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    • 2002
  • In this paper, we propose a new text watermarking method using space coding and PN sequence. A PN sequence generated from user message modifies the space between words in each line. The detection can be done without original text image using the average space with in the text. Experimental results show that proposed method has the invisible property and robustness to the attack such as the elimination of words in the text.

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Unstructured Data Quantification Scheme Based on Text Mining for User Feedback Extraction (사용자 의견 추출을 위한 텍스트 마이닝 기반 비정형 데이터 정량화 방안)

  • Jo, Jung-Heum;Chung, Yong-Taek;Choi, Seong-Wook;Ok, Changsoo
    • Journal of the Society of Korea Industrial and Systems Engineering
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    • v.41 no.4
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    • pp.131-137
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    • 2018
  • People write reviews of numerous products or services on the Internet, in their blogs or community bulletin boards. These unstructured data contain important emotions and opinions about the author's product or service, which can provide important information for future product design or marketing. However, this text-based information cannot be evaluated quantitatively, and thus they are difficult to apply to mathematical models or optimization problems for product design and improvement. Therefore, this study proposes a method to quantitatively extract user's opinion or preference about a specific product or service by utilizing a lot of text-based information existing on the Internet or online. The extracted unstructured text information is decomposed into basic unit words, and positive rate is evaluated by using existing emotional dictionaries and additional lists proposed in this study. This can be a way to effectively utilize unstructured text data, which is being generated and stored in vast quantities, in product or service design. Finally, to verify the effectiveness of the proposed method, a case study was conducted using movie review data retrieved from a portal website. By comparing the positive rates calculated by the proposed framework with user ratings for movies, a guideline on text mining based evaluation of unstructured data is provided.

Competitive intelligence in Korean Ramen Market using Text Mining and Sentiment Analysis

  • Kim, Yoosin;Jeong, Seung Ryul
    • Journal of Internet Computing and Services
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    • v.19 no.1
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    • pp.155-166
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    • 2018
  • These days, online media, such as blogospheres, online communities, and social networking sites, provides the uncountable user-generated content (UGC) to discover market intelligence and business insight with. The business has been interested in consumers, and constantly requires the approach to identify consumers' opinions and competitive advantage in the competing market. Analyzing consumers' opinion about oneself and rivals can help decision makers to gain in-depth and fine-grained understanding on the human and social behavioral dynamics underlying the competition. In order to accomplish the comparison study for rival products and companies, we attempted to do competitive analysis using text mining with online UGC for two popular and competing ramens, a market leader and a market follower, in the Korean instant noodle market. Furthermore, to overcome the lack of the Korean sentiment lexicon, we developed the domain specific sentiment dictionary of Korean texts. We gathered 19,386 pieces of blogs and forum messages, developed the Korean sentiment dictionary, and defined the taxonomy for categorization. In the context of our study, we employed sentiment analysis to present consumers' opinion and statistical analysis to demonstrate the differences between the competitors. Our results show that the sentiment portrayed by the text mining clearly differentiate the two rival noodles and convincingly confirm that one is a market leader and the other is a follower. In this regard, we expect this comparison can help business decision makers to understand rich in-depth competitive intelligence hidden in the social media.

A Group based Privacy-preserving Data Perturbation Technique in Distributed OSN (분산 OSN 환경에서 프라이버시 보호를 위한 그룹 기반의 데이터 퍼튜베이션 기법)

  • Lee, Joohyoung;Park, Seog
    • KIISE Transactions on Computing Practices
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    • v.22 no.12
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    • pp.675-680
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    • 2016
  • The development of various mobile devices and mobile platform technology has led to a steady increase in the number of online social network (OSN) users. OSN users are free to communicate and share information through activities such as social networking, but this causes a new, user privacy issue. Various distributed OSN architectures are introduced to address the user privacy concern, however, users do not obtain technically perfect control over their data. In this study, the control rights of OSN user are maintained by using personal data storage (PDS). We propose a technique to improve data privacy protection that involves making a group with the user's friend by generating and providing fake text data based on user's real text data. Fake text data is generated based on the user's word sensitivity value, so that the user's friends can receive the user's differential data. As a result, we propose a system architecture that solves possible problems in the tradeoff between service utility and user privacy in OSN.

A Study on Participatory Culture of Korean Webtoon Focused on User-Generated Images - (한국 웹툰의 참여 문화 연구 - 사용자 생성 이미지를 중심으로 -)

  • Kim, Juna;Kim, Su-Jin
    • Cartoon and Animation Studies
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    • pp.307-331
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    • 2016
  • Webtoon is the most popular cultural contents representing contemporary Korea. This study explores the cultural aspects of participatory culture surrounding Webtoon, and reveals the cultural implications of webtoon in contemporary Korea. Particularly, this study notes that the engagement of the participatory culture is formed from the user-generated images and analyzes the reproduction patterns of them. Chapter 2 analyzes the mimicking process of user-generated images based on the 'meme' concept. Especially based on the variation degree of text or image, the user-generated images could be classified into three types of 'completely variant', 'partly variant', and 'completely same'. Users use these images as one of the fun factor by transplanting them into daily messenger conversation. Chapter 3 reveals the cultural meaning which is derived from the process of user-generated images creation. In particular, this study notes that most of the user-generated images are mimicking the main character of the original webtoon, and analyzes the underlying desire of the mass based on the literary theory of Northrop Frye. The main readers of webtoon are petit-bourgeois living in Korean metropolitan, and the user-generated images also reflects the daily lives of these ordinary people. User-generated images of webtoon are imitating the original contents in a way of replicating or mutating the images or texts. Also, they are consumed and enjoyed as an amusing code among users. Especially by mimicking the appearance of the main character in a self-reflective way, they appeal to day-to-day sympathy of users. In that user-generated images reveal the desire of the public living in contemporary Korea, this study examines the cultural implication of webtoon.

Keywords Refinement using TextRank Algorithm (TextRank를 이용한 키워드 정련 -TextRank를 이용한 집단 지성에서 생성된 콘텐츠의 키워드 정련-)

  • Lee, Hyun-Woo;Han, Yo-Sub;Kim, Lae-Hyun;Cha, Jeong-Won
    • 한국HCI학회:학술대회논문집
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    • pp.285-289
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    • 2009
  • Tag is important to retrieve and classify contents. However, someone uses so many unrelated tags with contents for the high ranking In this work, we propose tag refinement algorithm using TextRank. We calculate the importance of keywords occurred a title, description, tag, and comments. We refine tags removing unrelated keywords from user generated tags. From the results of experiments, we can see that proposed method is useful for refining tags.

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Rating and Comments Mining Using TF-IDF and SO-PMI for Improved Priority Ratings

  • Kim, Jinah;Moon, Nammee
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.13 no.11
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    • pp.5321-5334
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    • 2019
  • Data mining technology is frequently used in identifying the intention of users over a variety of information contexts. Since relevant terms are mainly hidden in text data, it is necessary to extract them. Quantification is required in order to interpret user preference in association with other structured data. This paper proposes rating and comments mining to identify user priority and obtain improved ratings. Structured data (location and rating) and unstructured data (comments) are collected and priority is derived by analyzing statistics and employing TF-IDF. In addition, the improved ratings are generated by applying priority categories based on materialized ratings through Sentiment-Oriented Point-wise Mutual Information (SO-PMI)-based emotion analysis. In this paper, an experiment was carried out by collecting ratings and comments on "place" and by applying them. We confirmed that the proposed mining method is 1.2 times better than the conventional methods that do not reflect priorities and that the performance is improved to almost 2 times when the number to be predicted is small.

Visualization of Asthmatic Distribution Patterns in accordance with Administrative Dong Using GIS: a Case Study of Daegu (GIS를 활용한 행정동별 천식환자 분포특성의 시각화: 대구시의 사례 연구)

  • Shin, Ki-Dong;Um, Jung-Sup
    • Journal of Environmental Impact Assessment
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    • v.15 no.3
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    • pp.179-191
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    • 2006
  • The authors argue that the current Government Information System for asthmatics appears to be non-user friendly due to lack of the cartographic representation for the text based statistical data. Acknowledging these constraints, an operational, user-friendly map for asthmatic prevalence has been generated by combining existing statistical data with the administrative Dong boundary map under GIS environment. The Geographical User Interface, in particular, were ideally suited to deriving the major distribution patterns that more asthmatic prevalence tends to be occurred on conventional commercial district and industrial complex. A visual map using spatial modelling technology were generated to show the fact that some degree of increasing or decreasing trends of asthmatic prevalence already exists in the experimental sites. It could be used as an evidence to restrict initiation of development activities causing negative influence to asthma such as road construction. The result of this study would play a crucial role in improving the quality of environmental health information service if it is operationally introduced into the Government since the highly user-friendly interface provides a completely new means for disseminating information for asthmatics in a visual and interactive manner to the general public.

Text-To-Vision Player - Converting Text to Vision Based on TVML Technology -

  • Hayashi, Masaki
    • Proceedings of the Korean Society of Broadcast Engineers Conference
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    • pp.799-802
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    • 2009
  • We have been studying the next generation of video creation solution based on TVML (TV program Making Language) technology. TVML is a well-known scripting language for computer animation and a TVML Player interprets the script to create video content using real-time 3DCG and synthesized voices. TVML has a long history proposed back in 1996 by NHK, however, the only available Player has been the one made by NHK for years. We have developed a new TVML Player from scratch and named it T2V (Text-To-Vision) Player. Due to the development from scratch, the code is compact, light and fast, and extendable and portable. Moreover, the new T2V Player performs not only a playback of TVML script but also a Text-To-Vision conversion from input written in XML format or just a mere plane text to videos by using 'Text-filter' that can be added as a plug-in of the Player. We plan to make it public as freeware from early 2009 in order to stimulate User-Generated-Content and a various kinds of services running on the Internet and media industry. We think that our T2V Player would be a key technology for upcoming new movement.

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Text Mining and Sentiment Analysis for Predicting Box Office Success

  • Kim, Yoosin;Kang, Mingon;Jeong, Seung Ryul
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.12 no.8
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    • pp.4090-4102
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    • 2018
  • After emerging online communications, text mining and sentiment analysis has been frequently applied into analyzing electronic word-of-mouth. This study aims to develop a domain-specific lexicon of sentiment analysis to predict box office success in Korea film market and validate the feasibility of the lexicon. Natural language processing, a machine learning algorithm, and a lexicon-based sentiment classification method are employed. To create a movie domain sentiment lexicon, 233,631 reviews of 147 movies with popularity ratings is collected by a XML crawling package in R program. We accomplished 81.69% accuracy in sentiment classification by the Korean sentiment dictionary including 706 negative words and 617 positive words. The result showed a stronger positive relationship with box office success and consumers' sentiment as well as a significant positive effect in the linear regression for the predicting model. In addition, it reveals emotion in the user-generated content can be a more accurate clue to predict business success.